Code
import pandas as pd
df = pd.read_parquet("data/eda.parquet")import pandas as pd
skills_data = {
"Name": ["Binderiya", "Pranjul", "Pratham", "Panyang"],
"Python": [4, 4, 5, 3],
"SQL": [4, 4, 5, 4],
"Machine Learning": [2, 3, 2, 2],
"PySpark": [3, 3, 3, 3],
"Excel": [4, 5, 5, 4],
"Data Visualization": [5, 5, 3, 3],
"Power Bi/ Tableau": [4, 5, 3, 4],
"Version Control Git": [4, 4, 3, 3],
"ETL/Data pipeline": [3, 2, 1, 2],
"Communication": [4, 4, 5, 3],
"Project Management": [5, 5, 5, 3],
"Cloud Computing": [4, 4, 2, 2]
}
df_skills = pd.DataFrame(skills_data)
df_skills.set_index("Name", inplace=True)
df_skills| Python | SQL | Machine Learning | PySpark | Excel | Data Visualization | Power Bi/ Tableau | Version Control Git | ETL/Data pipeline | Communication | Project Management | Cloud Computing | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Name | ||||||||||||
| Binderiya | 4 | 4 | 2 | 3 | 4 | 5 | 4 | 4 | 3 | 4 | 5 | 4 |
| Pranjul | 4 | 4 | 3 | 3 | 5 | 5 | 5 | 4 | 2 | 4 | 5 | 4 |
| Pratham | 5 | 5 | 2 | 3 | 5 | 3 | 3 | 3 | 1 | 5 | 5 | 2 |
| Panyang | 3 | 4 | 2 | 3 | 4 | 3 | 4 | 3 | 2 | 3 | 3 | 2 |
import plotly.express as px
fig = px.imshow(
df_skills,
text_auto=True,
color_continuous_scale="YlGnBu",
aspect="auto"
)
fig.update_layout(
title="Team Skill Levels Heatmap",
xaxis_title="Skills",
yaxis_title="Team Members",
width=700,
height=400,
margin=dict(l=50, r=20, t=50, b=50)
)
fig.write_html(
"figures/skill_gap_plot1.html",
include_plotlyjs="cdn",
full_html=False
)import plotly.graph_objects as go
from IPython.display import IFrame
fig = go.Figure()
for name in df_skills.index:
values = df_skills.loc[name].tolist()
values += values[:1] # close the loop
fig.add_trace(go.Scatterpolar(
r=values,
theta=df_skills.columns.tolist() + [df_skills.columns[0]],
fill='toself',
name=name
))
fig.update_layout(
polar=dict(radialaxis=dict(visible=True, range=[0, 5])),
showlegend=True,
title='Team Skills Radar Chart'
)
fig.write_html(
"figures/skill_gap_plot2.html",
include_plotlyjs="cdn",
full_html=False
)From this radar chart visualization we can see that our team has a lot of room for improvement for skills like PySpark and Machine Learning. Also we can see that not a lot of our team mates are confident in their skills in Cloud Computing and ETL.
keywords = ['Data Analyst', 'Business Analyst', 'Data Engineering', 'Deep Learning',
'Data Science', 'Data Analysis','Data Analytics', 'Market Research Analyst'
'LLM', 'Language Model', 'NLP', 'Natural Language Processing',
'Computer Vision', 'Business Intelligence Analyst', 'Quantitative Analyst', 'Operations Analyst']
match = lambda col: df[col].str.contains('|'.join(keywords), case=False, na=False)
df['DATA_ANALYST_JOB'] = match('TITLE_NAME') \
| match('SKILLS_NAME') \
| match('SPECIALIZED_SKILLS_NAME')
df['DATA_ANALYST_JOB'].value_counts()DATA_ANALYST_JOB
False 38258
True 33083
Name: count, dtype: int64
import ast
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
# Safely apply literal_eval only to non-null values
df['SKILLS'] = df['SKILLS_NAME'].apply(lambda x: ast.literal_eval(x) if pd.notnull(x) else [])
data_skills = df[df['DATA_ANALYST_JOB']]['SKILLS'].explode().value_counts().reset_index()
data_skills.columns = ['Skill', 'Count']
fig = px.bar(data_skills, x='Skill', y='Count',
title="Top Skills",
labels={'Skill': 'Skill Name', 'Count': 'Frequency'},
color='Skill')
df_skills.index = df_skills.index.str.strip()from collections import defaultdict
# Lowercase everything
team_skills = [s.lower().strip() for s in df_skills.columns]
job_demand_raw = data_skills.copy()
job_demand_raw['Skill'] = job_demand_raw['Skill'].str.lower().str.strip()
# New dict to map cleaned team skill to total count from job postings
skill_demand_map = defaultdict(int)
for _, row in job_demand_raw.iterrows():
skill_in_posting = row['Skill']
count = row['Count']
for team_skill in team_skills:
if team_skill in skill_in_posting:
skill_demand_map[team_skill] += countteam_skills = [s.strip().lower() for s in df_skills.columns]
print("Team skills:", team_skills)
print(job_demand_raw['Skill'].head(10).tolist())
for skill_text in job_demand_raw['Skill'].head(10):
for team_skill in team_skills:
if team_skill in skill_text:
print(f" '{team_skill}' found in: '{skill_text}'")Team skills: ['python', 'sql', 'machine learning', 'pyspark', 'excel', 'data visualization', 'power bi/ tableau', 'version control git', 'etl/data pipeline', 'communication', 'project management', 'cloud computing']
['data analysis', 'sql (programming language)', 'communication', 'management', 'python (programming language)', 'tableau (business intelligence software)', 'dashboard', 'computer science', 'problem solving', 'power bi']
'sql' found in: 'sql (programming language)'
'communication' found in: 'communication'
'python' found in: 'python (programming language)'
sql 48590
communication 48980
python 22364
excel 19182
data visualization 14894
project management 14988
machine learning 8520
cloud computing 2610
pyspark 1026
dtype: int64
job_demand = pd.Series(skill_demand_map)
job_demand.name = "Count"
team_avg = df_skills.mean()
team_avg.index = team_avg.index.str.strip().str.lower()
# Now match only overlapping skills
common_skills = job_demand.index.intersection(team_avg.index)
team_avg = team_avg[common_skills]
job_demand = job_demand[common_skills]
# Normalize job demand
job_demand_normalized = 5 * (job_demand / job_demand.max())
job_demand_normalized.name = "Job Demand (Normalized)"
# Combine
comparison_df = pd.concat([team_avg, job_demand_normalized], axis=1)
comparison_df.columns = ["Team Average Skill", "Job Demand (Normalized)"]
comparison_df["Skill Gap"] = comparison_df["Job Demand (Normalized)"] - comparison_df["Team Average Skill"]
comparison_df.sort_values("Skill Gap", ascending=False, inplace=True)
comparison_df| Team Average Skill | Job Demand (Normalized) | Skill Gap | |
|---|---|---|---|
| communication | 4.00 | 5.000000 | 1.000000 |
| sql | 4.25 | 4.960188 | 0.710188 |
| machine learning | 2.25 | 0.869743 | -1.380257 |
| python | 4.00 | 2.282973 | -1.717027 |
| data visualization | 4.00 | 1.520416 | -2.479584 |
| excel | 4.50 | 1.958146 | -2.541854 |
| cloud computing | 3.00 | 0.266435 | -2.733565 |
| pyspark | 3.00 | 0.104737 | -2.895263 |
| project management | 4.50 | 1.530012 | -2.969988 |
import plotly.express as px
fig = px.bar(
comparison_df,
x='Skill',
y='Skill Gap',
color='Skill Gap',
color_continuous_scale='RdBu_r',
title='Skill Gaps: Job Market Expectations vs. Team Capability',
labels={'Skill Gap': 'Gap (Job Demand - Team Skill)', 'Skill': 'Skill'},
)
fig.add_hline(y=0, line_dash='dash')
fig.update_layout(
xaxis_tickangle=-45,
yaxis_title='Gap (Positive = Market expects more)',
font=dict(size=13),
height=500,
plot_bgcolor='white',
)
fig.write_html(
"figures/skill_gap_plot3.html",
include_plotlyjs="cdn",
full_html=False
)This bar chart compares our team’s average proficiency in key data-related skills against job market expectations. Skills with positive values (like communication and SQL) indicate areas where market demand exceeds our current capabilities. On the other hand, negative values highlight areas where the team is ahead or closely aligned with market needs. Notably, skills like Python, cloud computing, and project management show the largest gaps, suggesting priority areas for upskilling.